Small Sample Inferences on the Sharpe Ratio

© 2016 Taylor & Francis Group, LLC. This work deals with statistical inferences on the "Sharpe Ratio" (SR) based on small samples. We have considered point estimation, interval estimation, as well as hypothesis testing, assuming that a random sample is available from a normal distrib...

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Main Authors: Suntaree Unhapipat, Jun Yu Chen, Nabendu Pal
Other Authors: Mahidol University
Format: Article
Published: 2018
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/43308
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spelling th-mahidol.433082019-03-14T15:04:22Z Small Sample Inferences on the Sharpe Ratio Suntaree Unhapipat Jun Yu Chen Nabendu Pal Mahidol University Tamkang University University of Louisiana at Lafayette Business, Management and Accounting Mathematics © 2016 Taylor & Francis Group, LLC. This work deals with statistical inferences on the "Sharpe Ratio" (SR) based on small samples. We have considered point estimation, interval estimation, as well as hypothesis testing, assuming that a random sample is available from a normal distribution. Further, we study the robustness of our inferential methods when the data is thought to have come from other nonnormal distributions but is mistakenly modeled by the normal distribution. Results from a comprehensive simulation study have been provided to justify our observations and recommendations. Among other things, we have proposed a new estimator of SR that performs much better than the commonly used maximum likelihood estimator. Finally, some mutual fund datasets have been used for demonstration purposes to estimate SR in order to assess their monthly return performances. 2018-12-11T02:27:47Z 2019-03-14T08:04:22Z 2018-12-11T02:27:47Z 2019-03-14T08:04:22Z 2016-04-02 Article American Journal of Mathematical and Management Sciences. Vol.35, No.2 (2016), 105-123 10.1080/01966324.2015.1121847 01966324 2-s2.0-84960498281 https://repository.li.mahidol.ac.th/handle/123456789/43308 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84960498281&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Business, Management and Accounting
Mathematics
spellingShingle Business, Management and Accounting
Mathematics
Suntaree Unhapipat
Jun Yu Chen
Nabendu Pal
Small Sample Inferences on the Sharpe Ratio
description © 2016 Taylor & Francis Group, LLC. This work deals with statistical inferences on the "Sharpe Ratio" (SR) based on small samples. We have considered point estimation, interval estimation, as well as hypothesis testing, assuming that a random sample is available from a normal distribution. Further, we study the robustness of our inferential methods when the data is thought to have come from other nonnormal distributions but is mistakenly modeled by the normal distribution. Results from a comprehensive simulation study have been provided to justify our observations and recommendations. Among other things, we have proposed a new estimator of SR that performs much better than the commonly used maximum likelihood estimator. Finally, some mutual fund datasets have been used for demonstration purposes to estimate SR in order to assess their monthly return performances.
author2 Mahidol University
author_facet Mahidol University
Suntaree Unhapipat
Jun Yu Chen
Nabendu Pal
format Article
author Suntaree Unhapipat
Jun Yu Chen
Nabendu Pal
author_sort Suntaree Unhapipat
title Small Sample Inferences on the Sharpe Ratio
title_short Small Sample Inferences on the Sharpe Ratio
title_full Small Sample Inferences on the Sharpe Ratio
title_fullStr Small Sample Inferences on the Sharpe Ratio
title_full_unstemmed Small Sample Inferences on the Sharpe Ratio
title_sort small sample inferences on the sharpe ratio
publishDate 2018
url https://repository.li.mahidol.ac.th/handle/123456789/43308
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